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+---
+title: "NBHub | ABACUS+DeePKS Step-by-Step Practical Tutorial: Using the Perovskite System as an Example"
+date: 2024-10-17
+categories:
+- Tutorials@Notebooks
+mathjax: true
+---
+
+This Notebook will approach DeePKS from an application perspective, using the **perovskite system** as a case study. It systematically presents the complete process of **DeePKS model training and deployment**, including:
+
+1. **Preparation of labeled data** for the example system,  
+2. **Model training**, and  
+3. **Result analysis**.  
+
+Check out here: https://bohrium.dp.tech/collections/6242632852/
+
+**Tutorial Structure**
+
+Following a progression from simple to complex, this tutorial series is designed to guide readers step by step in learning DeePKS:  
+
+- **Single-element systems**:  
+   - Start with energy label training for systems containing the same type of element.  
+
+- **Multi-label training for single-element systems**:  
+   - Expand to training multiple labels (e.g., **energy**, **forces**, **stress**, and **band structure**) for single-element systems.  
+
+- **Real-world research systems**:  
+   - Transition to complex research systems (e.g., those with diverse elemental compositions), incorporating multi-label training for **energy**, **forces**, **stress**, and **band structure**.
+
+
+
+**Learning Outcomes**
+
+Through this tutorial, readers will:  
+
+- Gain a **deep understanding of the DeePKS method**,  
+- Master how to apply it to actual model training and deployment, and  
+- Equip themselves with essential skills to support future research.
+
+
+## Background
+### **First-Principles Calculations Based on KS-DFT**
+
+First-principles calculations based on **Kohn−Sham Density Functional Theory (KS-DFT)** have become one of the most widely used quantum mechanical methods at the atomic and molecular scales in recent decades. 
+
+The **accuracy of KS-DFT** is determined by the precision of the unknown terms in the total energy—namely, the **exchange-correlation functional**. Among the various approximations of exchange-correlation functionals—such as **LDA**, **GGA**, **meta-GGA**, and **hybrid functionals** [1-2]—achieving a balance between **accuracy** and **efficiency** has always been a challenge.
+
+- The most commonly used functional, such as the **PBE functional** under the **GGA approximation**, performs well in terms of computational efficiency but **often lacks accuracy** for specific systems.
+- On the other hand, **hybrid functionals** like **HSE06** offer higher accuracy but suffer from **lower computational efficiency**, making them impractical for handling large systems.
+
+---
+
+### **Opportunities with Artificial Intelligence**
+
+The **rapid development of artificial intelligence** (AI) has introduced new possibilities for representing and approximating high-dimensional complex functions. By leveraging **deep learning models** to bridge the gap between low- and high-accuracy functionals, it is now possible to achieve a good balance between efficiency and accuracy.
+
+---
+
+### **DeePKS Method**
+
+The **DeePKS method** is a deep learning-based functional correction approach developed to address this challenge [3-5]. Its key features are as follows:
+
+1. **Objective**: 
+   - DeePKS does not reconstruct the exchange-correlation functional itself. 
+   - Instead, it uses **machine learning techniques** to optimize low-accuracy functionals.
+
+2. **How it Works**:
+   - DeePKS learns the differences in **energy**, **forces**, **stress**, and **band structure** labels between:
+     - A **baseline functional** (e.g., PBE)
+     - A **target functional** (e.g., HSE06)
+   - This effectively combines the advantages of low- and high-accuracy calculations.
+
+3. **Key Benefits**:
+   - **Good balance between efficiency and accuracy**.
+   - **Low computational cost**:
+     - Correction terms are computationally as inexpensive as low-accuracy functionals.
+     - Far less expensive than high-accuracy functionals like HSE06.
+
+---
+
+### **Advantages in Practical Applications**
+
+- The **computational cost of correction terms** in DeePKS is comparable to that of low-accuracy functionals.
+- This makes DeePKS **significantly faster** than high-accuracy functionals, giving it a notable edge in practical applications.
+
+---
+
+### **Integration with DFT Software**
+
+During **DeePKS model training**, the update of model parameters alternates with the **self-consistent calculations** of first-principles methods.  
+This requires DeePKS to **seamlessly integrate** with existing **density functional theory software**.
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/DEEPKS.webp# pic_center width="100%" height="100%" /></center>
+
+
+Reference: 
+1.Kohn W, Sham L J. Self-consistent equations including exchange and correlation effects[J]. Physical review, 1965, 140(4A): A1133.
+2.Perdew J P, Burke K, Ernzerhof M. Generalized gradient approximation made simple[J]. Physical review letters, 1996, 77(18): 3865.
+3.https://github.com/deepmodeling/deepks-kit/tree/develop
+4.Chen Y, Zhang L, Wang H, et al. DeePKS: A comprehensive data-driven approach toward chemically accurate density functional theory[J]. Journal of Chemical Theory and Computation, 2020, 17(1): 170-181.
+5.Ou Q, Tuo P, Li W, et al. DeePKS Model for Halide Perovskites with the Accuracy of a Hybrid Functional[J]. The Journal of Physical Chemistry C, 2023, 127(37): 18755-18764.
+6.https://github.com/deepmodeling/abacus-develop
+7.Li W, Ou Q, Chen Y, et al. DeePKS+ ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials[J]. The Journal of Physical Chemistry A, 2022, 126(49): 9154-9164.